scholarly journals An offshore wind farm energy injection mastering using aerodynamic and kinetic control strategies

2018 ◽  
Vol 61 ◽  
pp. 00002 ◽  
Author(s):  
Djamel Ikni ◽  
Ahmed Ousmane Bagre ◽  
Mamadou Bailo Camara ◽  
Brayima Dakyo
Keyword(s):  
Power Quality ◽  
Wind Turbines ◽  
Wind Farm ◽  
Control Strategies ◽  
Wind Farms ◽  
Optimal Strategies ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Energy Storage Devices ◽  
Storage Devices

The injection of wind farm production into a grid, needs optimal strategies for energy transfer management. Usually, the power produced by the wind farms does not fulfil all the grid code requirements. The main problem is generally based on the way to reduce the impacts of power production fluctuations on the grid voltage and its frequency. To solve this problem, some authors suggest the use of an interface such as energy storage devices in order to compensate the wind power fluctuations. In fact, the storage devices installed between the wind farm and the grid can improve the power quality in terms of stability but in other hand the size and the cost of the system can be increased. In this paper, two solutions have been proposed in case the power quality produced by the wind farm is out of the grid code requirements. The improvement of the energy quality of an offshore wind farm without storage and connected to the grid is discussed. The proposed solution is to operate the wind turbines with a reserve of power. To distribute this reserve equitably among wind turbines, a proportional distribution algorithms has been developed. The results obtained show clearly the effectiveness of the strategy.

scholarly journals The Relation between Migratory Activity of Pipistrellus Bats at Sea and Weather Conditions Offers Possibilities to Reduce Offshore Wind Farm Effects

Animals ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 3457
Author(s):  
Robin Brabant ◽  
Yves Laurent ◽  
Bob Jonge Poerink ◽  
Steven Degraer
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Negative Relationship ◽  
Wind Farms ◽  
Weather Conditions ◽  
Seasonal Migration ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Offshore Wind Farms ◽  
Migratory Activity

Bats undertaking seasonal migration between summer roosts and wintering areas can cross large areas of open sea. Given the known impact of onshore wind turbines on bats, concerns were raised on whether offshore wind farms pose risks to bats. Better comprehension of the phenology and weather conditions of offshore bat migration are considered as research priorities for bat conservation and provide a scientific basis for mitigating the impact of offshore wind turbines on bats. This study investigated the weather conditions linked to the migratory activity of Pipistrellus bats at multiple near- and offshore locations in the Belgian part of the North Sea. We found a positive relationship between migratory activity and ambient temperature and atmospheric pressure and a negative relationship with wind speed. The activity was highest with a wind direction between NE and SE, which may favor offshore migration towards the UK. Further, we found a clear negative relationship between the number of detections and the distance from the coast. At the nearshore survey location, the number of detections was up to 24 times higher compared to the offshore locations. Our results can support mitigation strategies to reduce offshore wind farm effects on bats and offer guidance in the siting process of new offshore wind farms.

scholarly journals An equivalent modeling method for Offshore Wind Farms based on fault characteristics analysis

2019 ◽  
Vol 107 ◽  
pp. 01004
Author(s):  
Haiyan Tang ◽  
Guanglei Li ◽  
Linan Qu ◽  
Yan Li
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Modeling Method ◽  
Operating Conditions ◽  
Offshore Wind ◽  
Equivalent Model ◽  
Offshore Wind Farm ◽  
Offshore Wind Farms ◽  
Characteristics Analysis

A large offshore wind farm usually consists of dozens or even hundreds of wind turbines. Due to the limitation of the simulation scale, it is necessary to develop an equivalent model of offshore wind farms for power system studies. At present, the aggregation method is widely adopted for wind farm equivalent modeling. In this paper, the topology, electrical parameters, operating conditions and individual turbine characteristics of the offshore wind farms are taken into consideration. Firstly, the output power distribution of offshore wind farm, the voltage distribution of the collector system and the fault ride-through characteristics of wind turbines are analyzed. Then, a dynamic equivalent modeling method for offshore wind farms is developed based on the fault characteristics analysis. Finally, the proposed method is validated through time-domain simulation.

scholarly journals An analysis of offshore wind farm SCADA measurements to identify key parameters influencing the magnitude of wake effects

2017 ◽  
Vol 2 (2) ◽  
pp. 477-490 ◽  
Author(s):  
Niko Mittelmeier ◽  
Julian Allin ◽  
Tomas Blodau ◽  
Davide Trabucchi ◽  
Gerald Steinfeld ◽  
...  
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Average Power ◽  
Atmospheric Stability ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Offshore Wind Farms ◽  
Partial Load ◽  
Wake Effects

Abstract. For offshore wind farms, wake effects are among the largest sources of losses in energy production. At the same time, wake modelling is still associated with very high uncertainties. Therefore current research focusses on improving wake model predictions. It is known that atmospheric conditions, especially atmospheric stability, crucially influence the magnitude of those wake effects. The classification of atmospheric stability is usually based on measurements from met masts, buoys or lidar (light detection and ranging). In offshore conditions these measurements are expensive and scarce. However, every wind farm permanently produces SCADA (supervisory control and data acquisition) measurements. The objective of this study is to establish a classification for the magnitude of wake effects based on SCADA data. This delivers a basis to fit engineering wake models better to the ambient conditions in an offshore wind farm. The method is established with data from two offshore wind farms which each have a met mast nearby. A correlation is established between the stability classification from the met mast and signals within the SCADA data from the wind farm. The significance of these new signals on power production is demonstrated with data from two wind farms with met mast and long-range lidar measurements. Additionally, the method is validated with data from another wind farm without a met mast. The proposed signal consists of a good correlation between the standard deviation of active power divided by the average power of wind turbines in free flow with the ambient turbulence intensity (TI) when the wind turbines were operating in partial load. It allows us to distinguish between conditions with different magnitudes of wake effects. The proposed signal is very sensitive to increased turbulence induced by neighbouring turbines and wind farms, even at a distance of more than 38 rotor diameters.

scholarly journals Wind Turbine Clustering Algorithm of Large Offshore Wind Farms considering Wake Effects

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Binbin Zhang ◽  
Jun Liu
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Clustering Algorithm ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Wake Effect ◽  
Offshore Wind Farms ◽  
Power Simulation ◽  
Value Decomposition

This paper proposed the SVD (singular value decomposition) clustering algorithm to cluster wind turbines into some group for a large offshore wind farm, in order to reduce the high-dimensional problem in wind farm power control and numerical simulation. Firstly, wind farm wake relationship matrixes are established considering the wake effect in an offshore wind farm, and the SVD of wake relationship matrixes is used to cluster wind turbines into some groups by the fuzzy clustering algorithm. At last, the Horns Rev offshore wind farm is analyzed to test the clustering algorithm, and the clustering result and the power simulation show the effectiveness and feasibility of the proposed clustering strategy.

scholarly journals Key Issues on the Design of an Offshore Wind Farm Layout and Its Equivalent Model

2019 ◽  
Vol 9 (9) ◽  
pp. 1911
Author(s):  
Yuan-Kang Wu ◽  
Wen-Chin Wu ◽  
Jyun-Jie Zeng
Keyword(s):  
Wind Turbines ◽  
Wind Farm ◽  
Wind Farms ◽  
Offshore Wind ◽  
Equivalent Model ◽  
Offshore Wind Farm ◽  
Wake Effect ◽  
Wind Farm Layout ◽  
Farm Model ◽  
The Future

Offshore wind farms will have larger capacities in the future than they do today. Thus, the costs that are associated with the installation of wind turbines and the connection of power grids will be much higher, thus the location of wind turbines and the design of internal cable connections will be even more important. A large wind farm comprises of hundreds of wind turbines. Modeling each using a complex model leads to long simulation times—especially in transient response analyses. Therefore, in the future, simulations of power systems with a high wind power penetration must apply the equivalent wind-farm model to reduce the burden of calculation. This investigation examines significant issues around the optimal design of a modern offshore wind farm layout and its equivalent model. According to a review of the literature, the wake effect and its modeling, layout optimization technologies, cable connection design, and wind farm reliability, are significant issues in offshore wind farm design. This investigation will summarize these important issues and present a list of factors that strongly influence the design of an offshore wind farm.

Fatigue Life Analysis of Offshore Wind Turbine Support Structures in an Offshore Wind Farm

2018 ◽  
Author(s):  
Bryan Nelson ◽  
Yann Quéméner
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
The Body ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Analysis Tool ◽  
Support Structures ◽  
Offshore Wind Farm ◽  
Actuator Disk

This study evaluated, by time-domain simulations, the fatigue lives of several jacket support structures for 4 MW wind turbines distributed throughout an offshore wind farm off Taiwan’s west coast. An in-house RANS-based wind farm analysis tool, WiFa3D, has been developed to determine the effects of the wind turbine wake behaviour on the flow fields through wind farm clusters. To reduce computational cost, WiFa3D employs actuator disk models to simulate the body forces imposed on the flow field by the target wind turbines, where the actuator disk is defined by the swept region of the rotor in space, and a body force distribution representing the aerodynamic characteristics of the rotor is assigned within this virtual disk. Simulations were performed for a range of environmental conditions, which were then combined with preliminary site survey metocean data to produce a long-term statistical environment. The short-term environmental loads on the wind turbine rotors were calculated by an unsteady blade element momentum (BEM) model of the target 4 MW wind turbines. The fatigue assessment of the jacket support structure was then conducted by applying the Rainflow Counting scheme on the hot spot stresses variations, as read-out from Finite Element results, and by employing appropriate SN curves. The fatigue lives of several wind turbine support structures taken at various locations in the wind farm showed significant variations with the preliminary design condition that assumed a single wind turbine without wake disturbance from other units.

scholarly journals A cross-sectional survey of physical strains among offshore wind farm workers in the German exclusive economic zone

BMJ Open ◽  
2018 ◽  
Vol 8 (3) ◽  
pp. e020157 ◽  
Author(s):  
Marcial Velasco Garrido ◽  
Janika Mette ◽  
Stefanie Mache ◽  
Volker Harth ◽  
Alexandra M Preisser
Keyword(s):  
Wind Farm ◽  
Wind Farms ◽  
Exclusive Economic Zone ◽  
Offshore Wind ◽  
Upper Body ◽  
Offshore Wind Farm ◽  
Cross Sectional Survey ◽  
Cross Sectional ◽  
Heavy Loads ◽  
Under Construction

ObjectivesTo assess the physical strains of employees in the German offshore wind industry, according to job type and phase of the wind farm (under construction or operation).DesignWeb-based cross-sectional survey.SettingOffshore wind farm companies operating within the German exclusive economic zone.ParticipantsMale workers with regular offshore commitments and at least 28 days spent offshore in the past year (n=268).Outcome measuresPhysical strains (eg, climbing, noise, working overhead, with twisted upper body or in confined spaces, vibration, heavy lifting, humidity, odours).ResultsThe most frequently mentioned physical strain was ’climbing’ with 63.8% of the respondents reporting to be always or frequently confronted with climbing and ascending stairs during offshore work. Work as a technician was associated with a greater exposition to noise, vibrations, humidity, cold, heat, chemical substances, lifting/carrying heavy loads, transport of equipment, working in non-ergonomic positions and in cramped spaces, as well as climbing.Indeed, statistical analyses showed that, after adjusting for phase of the wind farm, age, nationality, offshore experience, work schedule and type of shift, compared with non-technicians, working as a technician was associated with more frequently lifting/carrying of heavy loads (OR 2.58, 95% CI 1.58 to 4.23), transport of equipment (OR 2.06 95% CI 1.27 to 3.33), working with a twisted upper body (OR 2.85 95% CI 1.74 to 4.69), working overhead (OR 2.77 95% CI 1.67 to 4.58) and climbing (OR 2.30 95% CI 1.40 to 3.77). Working in wind farms under construction was strongly associated with increased and decreased exposure to humidity (OR 2.32 95% CI 1.38 to 3.92) and poor air quality (OR 0.58 95% CI 0.35 to 0.95), respectively.ConclusionsWorkers on offshore wind farms constitute a heterogeneous group, including a wide variety of occupations. The degree of exposure to detrimental physical strains varies depending on the type of job. Technicians are more exposed to ergonomic challenges than other offshore workers.

scholarly journals Correlations of power output fluctuations in an offshore wind farm using high-resolution SCADA data

2021 ◽  
Vol 6 (4) ◽  
pp. 997-1014
Author(s):  
Janna Kristina Seifert ◽  
Martin Kraft ◽  
Martin Kühn ◽  
Laura J. Lukassen
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Power Output ◽  
Wind Farm ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Flow Conditions ◽  
Output Fluctuations ◽  
Power Difference ◽  
Time Correlations

Abstract. Space–time correlations of power output fluctuations of wind turbine pairs provide information on the flow conditions within a wind farm and the interactions of wind turbines. Such information can play an essential role in controlling wind turbines and short-term load or power forecasting. However, the challenges of analysing correlations of power output fluctuations in a wind farm are the highly varying flow conditions. Here, we present an approach to investigate space–time correlations of power output fluctuations of streamwise-aligned wind turbine pairs based on high-resolution supervisory control and data acquisition (SCADA) data. The proposed approach overcomes the challenge of spatially variable and temporally variable flow conditions within the wind farm. We analyse the influences of the different statistics of the power output of wind turbines on the correlations of power output fluctuations based on 8 months of measurements from an offshore wind farm with 80 wind turbines. First, we assess the effect of the wind direction on the correlations of power output fluctuations of wind turbine pairs. We show that the correlations are highest for the streamwise-aligned wind turbine pairs and decrease when the mean wind direction changes its angle to be more perpendicular to the pair. Further, we show that the correlations for streamwise-aligned wind turbine pairs depend on the location of the wind turbines within the wind farm and on their inflow conditions (free stream or wake). Our primary result is that the standard deviations of the power output fluctuations and the normalised power difference of the wind turbines in a pair can characterise the correlations of power output fluctuations of streamwise-aligned wind turbine pairs. Further, we show that clustering can be used to identify different correlation curves. For this, we employ the data-driven k-means clustering algorithm to cluster the standard deviations of the power output fluctuations of the wind turbines and the normalised power difference of the wind turbines in a pair. Thereby, wind turbine pairs with similar power output fluctuation correlations are clustered independently from their location. With this, we account for the highly variable flow conditions inside a wind farm, which unpredictably influence the correlations.

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